信阳网站开发公司,网络架构怎么看,asp网站默认后台,网站的网站建设公司标题椒盐噪声椒盐噪声
如果kkk是一幅数字图像中表示灰度值的比特数#xff0c;则灰度值可能是[0,2k−1][0, 2^k -1][0,2k−1]。椒盐噪声的PDF为#xff1a; P(z){Ps,z2k−1Pp,z01−(PsPp),zV(5.16)P(z) \begin{cases} P_s, z 2^k -1 \\ P_p, z0 \\ 1-(P_s P_…
标题椒盐噪声椒盐噪声
如果kkk是一幅数字图像中表示灰度值的比特数则灰度值可能是[0,2k−1][0, 2^k -1][0,2k−1]。椒盐噪声的PDF为
P(z){Ps,z2k−1Pp,z01−(PsPp),zV(5.16)P(z) \begin{cases} P_s, z 2^k -1 \\ P_p, z0 \\ 1-(P_s P_p), zV \end{cases} \tag{5.16}P(z)⎩⎪⎨⎪⎧Ps,Pp,1−(PsPp),z2k−1z0zV(5.16)
$0V2^k-1 $的任意整数
均值和方差为 zˉ(0)PpK(1−Ps−Pp)(2k−1)Ps(5.17)\bar z (0)P_p K(1 -P_s - P_p) (2^k -1) P_s \tag{5.17}zˉ(0)PpK(1−Ps−Pp)(2k−1)Ps(5.17) σ2(0−zˉ)2Pp(K−zˉ)2(1−Ps−Pp)(2k−1)2Ps(5.18)\sigma^2 (0-\bar z)^2 P_p (K - \bar z)^2 ( 1 -P_s -P_p) (2^k -1)^2 P_s \tag{5.18}σ2(0−zˉ)2Pp(K−zˉ)2(1−Ps−Pp)(2k−1)2Ps(5.18) 添加椒盐噪声的方法 令η(x,y)\eta(x, y)η(x,y)表示是一幅椒盐噪声图像其密度值满足式(5.16)。我们使用椒盐噪声污染图像f(x,y)f(x, y)f(x,y)的方法为 在fff中η\etaη为0的所有位置赋0在fff中η\etaη为2k−12^k-12k−1的所有位置赋2k−12^k-12k−1值保留fff中η\etaη为VVV的所有位置的值不变。 噪声密度 像素被盐粒或胡椒噪声污染的概率PPP为PPsPpPP_s P_pPPsPp。PPP称为噪声密度。
def salt_pepper_pdf(ps0.1, pp0.1):create salt and pepper PDF, math $$P(z) \begin{cases} P_s, z 2^k -1 \\ P_p, z0 \\ 1-(P_s P_p), zV \end{cases}$$param: z: input grayscale value of iamgeparam: v: float, while z v, probability of other valuesparam: ps: float, probability of the saltparam: pp: float, probability of the peppersalt_pepper np.zeros([3])salt_pepper[0] pssalt_pepper[2] ppsalt_pepper[1] 1 - (ps pp)return salt_pepper def add_salt_pepper(img, ps0.01, pp0.01):add salt pepper noise to imageparam: img: input image, uint8 [0, 255]param: ps: probability of salt noise, which is white noise, default is 0.01param: pp: probability of peper noise, which is black noise, default is 0.01return image with salt pepper noise, [0, 255]h, w img.shape[:2]mask np.random.choice((0, 0.5, 1), size(h, w), p[pp, (1-ps-pp), ps])img_out img.copy()img_out[mask1] 255img_out[mask0] 0return img_outdef add_salt_pepper_1(img, prob):output np.zeros(img.shape, np.uint8)thres 1 - probfor i in range(img.shape[0]):for j in range(img.shape[1]):rdn np.random.random()if rdn prob:output[i][j] 0elif rdn thres:output[i][j] 255else:output[i][j] img[i][j]return output# 椒盐噪声
z np.array([0, 128, 255])
ps 0.1
pp 0.3
k 8
z_ 0 * pp k * (1 - ps - pp) (2**k - 1) * ps
sigma (0 - z_) ** 2 * pp (k - z_) ** 2 * (1 - ps - pp) (2**k -1) ** 2 * psprint(fz_ - {z_}, sigma - {sigma})salt_pepper salt_pepper_pdf(psps, pppp)plt.figure(figsize(9, 6))
plt.bar(z, salt_pepper), plt.xticks([]), plt.yticks([])
plt.show()z_ - 30.3, sigma - 7076.301# 椒盐噪声可随意控制胡椒与盐粒的概率大小
# img_ori np.ones((512, 512)) * 128
img_ori cv2.imread(DIP_Figures/DIP3E_Original_Images_CH05/Fig0503 (original_pattern).tif, 0)img_salt_pepper add_salt_pepper(img_ori, ps0.05, pp0.02)plt.figure(figsize(9, 6))
plt.subplot(121), plt.imshow(img_ori, gray, vmin0, vmax255), plt.xticks([]), plt.yticks([])
plt.subplot(122), plt.imshow(img_salt_pepper, gray, vmin0, vmax255), plt.xticks([]), plt.yticks([])plt.tight_layout()
plt.show()hist, bins np.histogram(img_salt_pepper.flatten(), bins255, range[0, 255], densityTrue)
bar plt.bar(bins[:-1], hist[:])# 一些重要的概率密度函数
a 2
b 8z np.linspace(0, 10, 200)gaussian gauss_pdf(z)
rayleigh rayleigh_pdf(z, aa, bb)
ireland ireland_pdf(z, aa, bb-6)
exponent exponential_pdf(z, aa)
average average_pdf(z, aa, bb)#椒盐
ps 0.2
pp 0.3
salt_pepper salt_pepper_pdf(psps, pppp)show_list [gaussian, rayleigh, ireland, exponent, average, salt_pepper]fig plt.figure(figsize(16, 8))for i in range(len(show_list)):ax fig.add_subplot(2, 3, i1)if i 5:z np.array([0, 100, 255])ax.bar(z, eval(show_list[i]), width2), ax.set_xticks([0, 100, 255]), ax.set_yticks([0, eval(show_list[i]).max() ])ax.set_xlim(-1, 257), ax.set_ylim([0, eval(show_list[i]).max() 0.05]), ax.set_title(show_list[i])else:ax.plot(z, eval(show_list[i])), ax.set_xticks([0, 10]), ax.set_yticks([0, eval(show_list[i]).max() ])ax.set_xlim(0, 10), ax.set_ylim([0, eval(show_list[i]).max() 0.05]), ax.set_title(show_list[i])
plt.tight_layout()
plt.show()一般重要噪声的例子 高斯噪声 由电子电路及光照不足和/或高温引起的传感器噪声等因素导致的 瑞利噪声 有助于表征距离成像中的噪声现象 指数和伽马密度 在激光成像中广泛应用 冲激噪声 出现在成像期间的快速瞬变如开关故障 均匀密度 是对实际情况最起码的描述
# 一些重要的噪声
img_ori cv2.imread(DIP_Figures/DIP3E_Original_Images_CH05/Fig0503 (original_pattern).tif, 0)img_gauss add_gaussian_noise(img_ori, mu0, sigma0.05)
img_rayleigh add_rayleigh_noise(img_ori, a1)
img_gamma add_gamma_noise(img_ori, scale2)
img_exponent add_exponent_noise(img_ori, scale3)
img_average add_average_noise(img_ori, mean10, sigma1.5)
ps 0.05
pp 0.02
img_salt_pepper add_salt_pepper(img_ori, psps, pppp)show_list [img_gauss, img_rayleigh, img_gamma, img_exponent, img_average, img_salt_pepper]fig plt.figure(figsize(14.5, 20))for i in range(len(show_list)):if i 3:# 显示图像ax fig.add_subplot(4, 3, i 3 1)ax.imshow(eval(show_list[i]), gray), ax.set_xticks([]), ax.set_yticks([]), ax.set_title(show_list[i].split(_)[-1])# 对应图像的直方图ax fig.add_subplot(4, 3, i 1 6)hist, bins np.histogram(eval(show_list[i]).flatten(), bins255, range[0, 255], densityTrue)bar ax.bar(bins[:-1], hist[:]), ax.set_xticks([]), ax.set_yticks([]),else:# 显示图像ax fig.add_subplot(4, 3, i 1)ax.imshow(eval(show_list[i]), gray), ax.set_xticks([]), ax.set_yticks([]), ax.set_title(show_list[i].split(_)[-1])# 对应图像的直方图ax fig.add_subplot(4, 3, i 1 3)hist, bins np.histogram(eval(show_list[i]).flatten(), bins255, range[0, 255], densityTrue)bar ax.bar(bins[:-1], hist[:]), ax.set_xticks([]), ax.set_yticks([]),plt.tight_layout()
plt.show()
